Types of Research Data Collection (Source)

• Primary Data Source
  • 1st hand information
  • not changed by any individual
  • not published yet
  • directly collected by authors
• Secondary Source
  • published data
  • what literature review is based on
  • reviewed by authors(you)

Examples of data collection methods by category

• Primary Data Collection Examples
  • Questionnaires
  • Interviews
  • Focus Group Interviews
  • Observation
    • participant/non-participant
    • aware/non-aware
  • Survey
  • Statistical Methods
  • Experimental Methods
• Secondary Data Collection Examples
  • Published Papers/Sources
  • Databases
  • Books
  • General websites
  • Unpulibhsed personal records
  • Census data/population statistics

Primary Data Collection Methods: Desining Questionnaires

• a set of questions and secure answers from respondants
• often analyzed by statsitical methods
• consistency in questionnaires make cross-sectional analysis easy

Types of questions designed to measure variables in survey:

• Close-end questions
  1. Two-option aka dichotomous scales
  2. More than two options: Nominal-polychromous
  3. Ordinal-Polytomous scale
  4. Continous or bounded types
• Open-end questions
  • setence completion
  • open-ended questions with free text responses

Polytomous Variables, aka Options for Multiple Choice Questions

Statistical term that refers to a categorical variable with more than two possible categories or levels

Common in: Survey research ,healthcare, market research and education

Characteristics:

• Categorical data: limited/distinct values or categories that are mutually exclusive
• Always more than two options(binary)
• Ordered and unordered (natural hiearchy)
• Statistical analyses: e.g. logistic regression, cluster analysis for patterns and trends
• Convertable to binary variables

Purpose:

• Understanding complex phenomena: patterns and trends in the data collected
• Provide insights to better categorization of data
• Statistical analyses: Relationships between variables: e.g. logistic regression to predict probability
• Market segmentation: e.g. customers to preference, behavior, demographic groups

Polytomous Variable (Continued)

When to use Polytomous Variables

• Measuring attitudes or perceptions: for more nuanced perceptions
• Putting definitions on categorizzing data: greater variability
• Analyzing relationships between variables: job satisfication & job performance
• Need to capture complex phenomena

Limitations

• Power of explanation limited by available options
  • e.g. satisfactory survey & number of enrolled students, what about change?
• subjectivity from questionnaire design and interpretation of results
• response bias
• small sample sizes
  • calculating the appropriate size of population needed
  • amount of respondants needed to reach statistical significance (reject null hypothesis, not covered by current class, leverage sample size calculator online)

Primary Data Collection: Designing Questionnaires

Face-to-face, paper-and-pencil or remote, make sure your data collected is: - well-organized and - easily accessible for analysis.

General rules for constructing a questionnaire:

Dos:

• questions should be short and simple
• provide clear navigation to avoid difficulty in reading and motivate answering
• use positive sentences
• add open-answer possibility after provideing listed answers
• improve reliability by selecting appropriate words
• explain importance of the questionnaire
• order your questions to solicit the right answers (sensitive to follow concrete/innocuous ones)

Do-nots:

• use more than one question (double-barreled) in one item
• make assumptions for the respondents
• lead the respondant to answers with clues, suggestions and hints

Steps involved in designing a questionnaire

Primary Data Collection: Interviews

Face-to-face and remote (telephone/zoom) interviews and merit/demerits (Kabir, 2016).

Good for complex or sensitive concepts and need detailed and high-status information (Frechtling, 2020).

Types of interviews by structure:

• structured interviews: standardized questions that are pre-prepared
• semi-structured interviews: conducted based on guide but goes beyond list of questions
• unstructured interviews: informal, casual conversations

Rundown of an interview process

Primary Data Collection: Focus Group Discussion (FGD)

• Mixture of interview and observation
• Used to discover human behavior, attitudes and respondents facing a particular concept
• 6-12 people in each group with shared characteristics
• Mediator aims to stimulate and discover the behavior of the participats and reasons for each behavior using the social dynamic of the group

FGD: Strengths and Weaknesses

Strengths:

• discover social, health and cultural concepts
• literacy of individuals non-issue
• suitable to explore complex subjects
• useful to develop hypotheses

Weaknesses:

• expensive and time-consuming
• privacy risks
• confined by readiness of facilitator/mediator
• domination of limited individuals in focus group (Frechtling, 2002, Kabir, 2016)

Survey and questionnaire: What’s the difference?

• Questionnaire is the written set of questions.
• Survey is both the set of questions and the process of collecting, aggregating and analyzing the responses from those questions.

Survey: Example survey accompanying sheet

Good & Bad Survey Questions: Let’s try out how to conduct surveys

Context: Surveying respondants on their religious beliefs and life styles

1. How religious are you?

• vague, not sure what is being asked
• How would you rate your level of spirituality? Pick a value between 0 to 4

2. What do you think about smoking on campus?

• vague, too many possible answers
• Do you believe that all buildings on campus should be designated as smoke-free?

3. How important is spirituality in your life? Pick a value between 1(not at all) to 5 (Very much so), (3: somewhat important).

• leading, suggests that spirituality is important
• What role does spirituality play in your life? Pick between 1(not important) to 5(very important) with 3 being somewhat important

Recap: Tips for effective surveys:

1. Avoid ambiguity
2. Avoid leading questions
3. Avoid lengthy surveys and very long responses
4. How will your initial questions influence answers to subsequent ones?
5. Think carefully about sampling techniques
6. Seek to achieve highest rate possible
7. Standardize administration procedures
8. Guarantee anonymity (or confidentiality at minimum)
9. Seek measures of reliability
10. Assess validity.

Primary Data Collection: Case Studies

• Opportunity to investigate issues deeply and descriptively.
• technically not a research method
• combination of various methods to form proper understanding of the proposed case:
  • gather data through qualitative methods including interviews and surveys
  • acquire secondary-sourced data e.g. essays and diaries for analysis
  • personally provided notes can be also utilized alongside official ones

Information Sources for Case Studies

• Direct observation from subjective evaluations
  • Single or group of observers
• Participant observation
  • Researcher participate in the setting like other under-study people and observe happenings from a closer perspective
• Conduct interviews with survey-type questions that are structured based on more open-ended question sets.
• Use various census and survey records, newspapers, letters, instruments, tools, etc.

Merit and Challenges of working with Case Studies

Pros/Merits:

• Combines the strengths of multiple research methods
• Consider research from various time frame: past, present and future
• Provide explanation about the changes and impacting factors that are not readily available

Cons/Challenges:

• Complex processes, time consuming and expensive
• No clear limit on when to stop collecting data
• The assumption taken may not always be realistic or data tested in that context
• Usually requires expert and trained conducting teams
• Over-interpretation and over-generalizing issues can happen (Taherdoost, 2021)

Primary Data Collection: Experimental

1. Laboratory/Controlled:
   • Highest control over study design and process,
   • gain precise and accurate data
2. Field:
   • real-life situation,
   • variables are manipulated still but
   • your control is lower than Scenario 1
3. Natural experiment:
   • no control over variables/environmental setting
   • very low reproducibility

Secondary dcata collection methods

Data gathered from published sources.

Challenges of Data Collection Process

1. Location of data collection

   • neutral location
   • participants to feel free to provide their responses

2. Literacy of Participants and Langauge of Questions

   • Design of questions is appropriate for the literacy level of participants
   • may require pilot tests to confirm (added costs)

3. Timing

   • Duration of test needs to be long enough to yield reasonable results
   • Short enough to maintain the engagement of the participants

4. Sensitivity of Data

   • Privacy of the Participants (Unique identifier)
   • Protecting personal information through promises and icebreakers and examples

Data Visualization: Introducing Pygwalker

# In Jupyter:
import pandas as pd
df=pd.read_csv('data/tcdatasample.csv')

!pip install pygwalker
import pygwalker as pyg
pyg.walk(df)

#Set up as a separate datasample.py file. Run with `streamlit run datasample.py`
from pygwalker.api.streamlit import StreamlitRenderer
import pandas as pd
import streamlit as st
 
# Adjust the width of the Streamlit page
st.set_page_config(
    page_title="Use Pygwalker In Streamlit",
    layout="wide"
)
# Import your data
df = pd.read_csv("/Users/guo/tprojs/desn2003/data/tcdatasample.csv")
 
pyg_app = StreamlitRenderer(df)
 
pyg_app.explorer()

Data Download

Let’s practice data collection/design concepts

Several years ago, a group of students at University of Central Arkansas conducted a study in which they observed the rate at which cars failed to stop at a campus stop sign and recorded whether the car had a student parking decal or a faculty/staff parking decal. This is obviously not fitting for Hong Kong context. Let’s perhaps picture a study of the rate of jay-walking at a traffic light instead - and record whether the pedestrian who crossed is a student/staff/tourist/local resident. Use what we have covered today to answer questions 1-7:

1. Which method of observation would be best? Justify your answer. Hint: back to participant/direct observations.
2. How would you schedule observations?
3. Define the categories of behavior that you would observe
4. Describe how you would optimize and measure the reliability of observations, including the use of independent observers and calculation of interobserver agreement.
5. Describe how you could use equipment for observation rather than human observers, what are the advantages and disadvantages?
6. Describe how you might use public records to answer the same research question. What might be some limitations of this approach
7. Describe how you might use a survey method to answer the same research question. What might be some limitations of this approach?

References

1. Frechtling, J. (2002). An overview of quantitative and qualitative data collection methods The 2002 user-friendly handbook for project evaluation (pp. 43-62).
2. Hox, J. J., & Boeije, H. R. (2005). Data collection, primary versus secondary Encyclopedia of social Measurement (Vol. 1): Elsevier.
3. Data collection challenges (2005).
4. Kabir, S. M. S. (2016). Methods Of Data Collection Basic Guidelines for Research: An Introductory Approach for All Disciplines (first ed., pp. 201-275).
5. Olsen, W. (2012). Data collecti on: Key debates and methods in social research (Vol. 1): Sage.
6. Pandey, P., & Pandey, M. M. (2015). Research Methodology: Tools and Techniques (Vol. 1). Romania: Bridge Center.
7. Rimando, M., Brace, A. M., Namageyo-Funa, A., Parr, T. L., Sealy, D.-A., Davis, T. L., . . . Christiana, R.W. (2015). Data collection challenges and recommendations for early career researchers. The Qualitative Report, 20 (12), 2025-2036.
8. Taherdoost, H. (2016a). How to design and create an effective survey/questionnaire; A step by step guide. International Journal of Academic Research in Management (IJARM), 5 (4), 37-41.
9. Taherdoost, H. (2016b). Measurement and scaling techniques in research methodology; survey/questionnaire development. International Journal of Academic Research in Management (IJARM),6 (1), 1-5.
10. Taherdoost, H. (2016c). Sampling methods in research methodology; how to choose a sampling technique for research. International Journal of Academic Research in Management (IJARM), 5 (2), 18-27.
11. Taherdoost, H. (2019). What is the best response scale for survey and questionnaire design; review of different lengths of rating scale/attitude scale/Likert scale. International Journal of Academic Research in Management (IJARM), 8 (1), 1-10.
12. Taherdoost, H. (2021). Handbook on Research Skills: The Essential Step-By-Step; Guide on How to Do a Research Project (Kindle ed.): Amazon.